Structures et relations spatiales entre les images aériennes multi-spectrales, les propriétés du sol et les rendements de grandes cultures
Bibliographic record
Abstract
Aerial numerical images captured under critical soil conditions and crop stages are promising tools for crop and soil zone management if (1) images reflect the spatial structure of stable soil properties and crop yield potential and (2) spatial scales of variability of soil properties and crop yield potential are manageable. To asses the potential of numeric aerial images to support soil and crop zone management, spatial integration and geostatistical analysis in time and frequency domains of yield, topography, physico-chemical soil properties and aerial multi-spectral images, captured in spring and summer, have been supported for four fields from the Bois-Francs region, in Québec, and 2 years of production. Early summer images predicted monitored yield for experimental sites demonstrating significant spatial structure in crop productivity. Early spring images captured over harrowed soils and numerical elevation models revealed spatial structures of soil physico-chemical properties. Scales of spatial variability are generally compatible with soil and crop zone management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".